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Record W4282937630 · doi:10.1111/1911-3846.12797

Tax Incidence and Tax Avoidance*

2022· article· en· W4282937630 on OpenAlexvenueno aff
Scott Dyreng, Martin Jacob, Xu Jiang, Maximilian A. Müller

Bibliographic record

VenueContemporary Accounting Research · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate taxTax avoidanceTax incidenceBusinessMonetary economicsTax reformDouble taxationIndirect taxShareholderLabour economicsEconomicsPublic economicsFinanceCorporate governance

Abstract

fetched live from OpenAlex

ABSTRACT Economists broadly agree that the economic burden of corporate taxes is not entirely borne by shareholders but also borne in part by employees and consumers. We examine corporate tax avoidance in a setting where shareholders do not bear the entire economic burden of the corporate tax. We show that the relation between corporate tax incidence and corporate tax avoidance depends on the elasticity of labor supply, the productivity of capital relative to labor, and the tax deductibility of labor and capital. These forces operate through two channels ( firm scale and input mix ), making the actual association between tax avoidance and incidence an empirical question. We find that firms whose shareholders bear less of the economic burden of corporate taxes engage in less avoidance. Our findings suggest that maximizing after‐tax profits might entail less tax avoidance if shareholders do not entirely bear the corporate tax burden. In particular, when the incidence of the corporate tax falls on the firm, firms avoid more taxes. This tendency is stronger if firms use a higher level of capital input, if the deductibility of the cost of capital investment is limited, if firms have high capital productivity, or if tax enforcement is strong.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.068
GPT teacher head0.305
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations73
Published2022
Admission routes1
Has abstractyes

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